Category: Reinforcement Learning
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Part 6: Beyond Simulation: Addressing the Sim-to-Real Gap
Bridge the sim-to-real gap: domain randomization cut our robot failure rate by 73%. Here's the exact transfer learning pipeline we used.
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Reward Engineering for Financial and Robotic RL Tasks
Reward shaping doubled our RL agent's success rate. Compare dense vs sparse rewards, risk-adjusted trading, and curriculum learning with benchmarks.
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Stable Baselines3: Tips for Training Robust RL Agents
Train production-ready RL agents with Stable Baselines3: hyperparameter tuning tricks, TensorBoard monitoring, and debugging NaN rewards fast.
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Part 3: Policy Gradient vs. Q-Learning: Choosing the Right Agent
DQN vs PPO vs SAC: when each algorithm wins. Math foundations, code examples, and a decision tree for picking the right RL agent for your task.
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Building Custom Gym Environments with OpenAI Gymnasium
Build a custom stock trading Gym environment from scratch: state design, reward engineering, and vectorization. Working code ready to train agents.
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Part 1: The Core of RL: Markov Decision Processes (MDP) Explained
Master MDPs and Bellman equations: the math foundation of RL explained with Python code. Understand value functions and dynamic programming clearly.
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Multi-Agent RL Guide: Cooperative and Competitive Learning
QMIX vs self-play: benchmarked 3 MARL algorithms on StarCraft micromanagement. One approach dominated cooperative tasks, the other competitive.
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Model-Free vs Model-Based RL: ์ํ ํจ์จ์ฑ ๋น๊ต ๊ฐ์ด๋
Model-Free vs Model-Based RL: which needs fewer samples? Atari/MuJoCo benchmarks reveal the trade-offs you need to know before coding.